Results and Discussion

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31.4.1 Evaluation of SO2 and TSP concentrations and meteorological parameters in 1995-1997 winter season

SO2 and TSP were monitored between 1995 and 1997 in the city of Erzurum. For SO2 and TSP, winter season limit values are 250 p.g/m3 and 200 p.g/m3, respectively, according to the Turkish Air Quality Protection Regulation (MOE, 1986). Daily averages, standard deviations, minimum and maximum concentrations for the city center are summarized in Table 31.1. The daily air pollutants concentration were graphed together with short-term limit (STL) values as 400 pg/m3 for SO2 and 300 pg/m3 for TSP in Turkish Air Quality Protection Regulation (MOE, 1986) and are shown in Fig. 31.2.

Table 31.1 The means, maximum, minimum, and standard deviation of meteorological parameters and SO2 and TSP concentration between years 1995 and 1997.

Mean Maximum Minimum Standard Number deviation Of observation

SO2 concentration (pg/m ) TSP concentration (pg/m3) Temperature (oC) Relative humidity (%) Wind speed (m/s)

252

1915

13

317

185

112

749

0.72

116

185

-5.1

9

-26

7.54

185

60

98

18

21

185

0.85

6.83

0

0.95

185

Fig. 31.2 (a) The daily change of SO2 and (b) TSP concentration in winter periods (STL: short-term limit value).

The monthly averages of SO2 and TSP values in 1995-1996 are higher than the next winter season due to meteorological parameters. Maximum SO2 and TSP concentrations occur in January, 645 and 210 |lg/m3, respectively. These high SO2 and TSP values were due to low temperature (-11°C) and low winds (0.6 m/s). The pollutants levels are high also in other colder months as December and February. In winter periods of 1995-1997, the SO2 and TSP concentration values exceed the short-term limit (STL) of Turkish Air Quality Protection Regulation, and corresponding meteorological parameter values in these days, are listed in Table 31.2.

Table 31.2 The means, maximum, minimum, and standard deviation of SO 2and TSP concentrations that together exceed STL and corresponding meteorological values.

Mean

Maximum

Minimum

Standard deviation

Number of observation

SO2 concentration

1310

1915

417

512

7

(pg/m3)

TSP concentration

380

476

306

65

7

(pg/m3)

Temperature (oC)

-12

1.1

-23.42

9.61

7

Relative Humidity (%)

83

89

77

4

7

Wind speed (m/s)

0.18

0.63

0.10

0.18

7

SO2 together with TSP values exceeded the STL for 7 days during two winter periods. The values of daily average temperature (-13°C) and wind speed (0.18 m/s) in these days were determined to be below the seasonal average. These episode days may be attributed to the consumption of more fuel due to lower temperatures, resulting in high SO2 emission and also unfavorable meteorological factors. In addition to the meteorological factors, an inversion event is seen frequently in winter season due to presence of the high mountains surrounding Erzurum which affects air pollutant distribution negatively (Turalioglu et al., 2005).

Air pollution can affect our health either short term or long term. Some effects of air pollutants on human health are shown in Table 31.3. It was considered that 7 days in Table 31.2 were risky days for the chronic bronchitis patients in Er-zurum city. Minimum TSP concentrations and SO2 concentrations for those days were over 300 |g/m3 and 400|g/m3, respectively.

Table 31.3 Effects of suspended particulate matter in atmosphere.

Concentration (pg/m3)

Effects

Remarks

100-130

Beginning of respiratory tract disease at children

SO2 concentration > 120 pg/m3

300 (daily averaged)

Beginning of serious crisis at chronic bronchitis patients

SO2 concentration > 630 pg/m3

750 (daily averaged)

Increasing deaths

SO2 concentration > 715

pg/m3

Source: Muezzinoglu (2000).

Source: Muezzinoglu (2000).

In addition, average SO2 and TSP concentrations presented for winter season in Table 31.1 were 252 |g/m3 and 112 |g/m3, respectively. These levels of air pollutants can affect children during the winter season. It has been explained that the long-term threshold concentration for forest varied between 100 and 150 pg/m3. In lower value than the threshold concentration, damages for leaves of coniferous trees especially pine, fir, and spruce have been determined (McLaughlin, 1985). It is known that coniferous trees are more sensitive than broad leaf trees during winter season. The most sensitive trees for SO2 pollution are fir (Abies sp.), spruce (Picea L.), willow (Salix L.). Some of the sensitive trees for SO2 pollution are lime (Tilia L.), beech (Fagus L.), pine (Pinus L.). Some of the less sensitive trees for SO2 pollution are oak (Quer-cus), poplar (Populus L.), pear (Pyrus L.), baxwood (Carpinus betulus), juniper (Juniperus L.). It was considered that trees affected by air pollution levels during winter season in Erzurum city were willow (Salix babylonica), ash tree (Fraxinus excelsior L.), ash tree (F. americana L), pine (Pinus sylvestris), poplar (Populus nigra 'Italica'), poplar (Populus alba), elm (Ulmus glabra), boxelder (Acer negundo L.).

31.4.2 Relationship between SO2, TSP, and meteorological factor

The relationship between SO2, TSP, and meteorological parameters (temperature, wind speed, relative humidity) and previous day's pollutant concentration in 1995-1997 winter periods was investigated by stepwise multiple linear regression analysis. The correlation (r) between daily average SO2, TSP concentrations and daily average meteorological parameters and previous' day pollutants concentration is shown in Table 31.4.

Table 31.4 Correlation (r) between daily average SO 2 TSP, and daily average meteorological parameters in this study and other studies.

Pollutants Temperature Wind Relative Previous References

Table 31.4 Correlation (r) between daily average SO 2 TSP, and daily average meteorological parameters in this study and other studies.

Pollutants Temperature Wind Relative Previous References

speed

humidity

day's Concentration

SO2

-0.45

-0.31

-0.46

0.77

This study

TSP

-0.37

-0.30

0.09

0.42

This study

SO2

-0.78

-0.49

0.03

0.84

Turalioglu et al.

(2005)

TSP

-0.79

-0.64

0.13

0.53

Turalioglu et al.

(2005)

SO2

-0.82

-0.42

-0.19

Gupta et al.

(2008)

SO2

-0.20

0.20

Elminir (2005)

PM10

-0.41

0.25

Elminir (2005)

PMm

0.24-0.48

0.23-

0.08-

Vardoulakis and

0.55 0.39 Kassomenos

0.55 0.39 Kassomenos

The SO2 and TSP concentrations as a function of meteorological parameters are shown in Fig. 31.3a-h. As seen from Table 31.4, it was found that the moderate correlation occurs between SO2, TSP, and temperature for whichp <0.01. It is obvious that the pollutant concentrations decrease effectively with high increasing temperature. There is a negative correlation between pollutants (SO2 and TSP) concentrations and wind velocity (p <0.01 for SO2 and p <0.05 for TSP). SO2 and TSP concentrations decrease with increasing wind speed, as seen from Fig. 31.3c,g. The relative humidity is a weakly linked parameter to TSP (p <0.01). Also the relative humidity is a moderately linked parameter to SO2 (p <0.05). The correlation between the previous day's TSP and SO2 values and actual concentration is investigated and found as 0.77 and 0.42, respectively, and their correlations are shown in Fig. 31.3a and 31.3e (p <0.01). Also, the findings of others related to correlations between SO2, particulate matter, and meteorological parameters are shown in Table 31.4. It is seen from Table 31.4 that correlations of SO2 and TSP for temperature, wind speed, and relative humidity obtained at this study are similar to those found in other studies. Also the correlation between SO2 and previous day's SO2 (this study) is near that found at Erzurum by Turalioglu et al. (2005). It is obvious that the correlation values are highly site specific (Vardoulakis and Kassomenos, 2009).

31.4.3 The regression analysis

The SO2 and PM concentrations are considered to be dependent variables, whereas meteorological factors and previous day's air pollutants concentration are considered to be independent variables. Multiple regression analysis was applied for each day, and it was shown that there are relationships between SO2 concentration and meteorological factors and previous day's SO2 concentration. In this study, since the relative humidity gave weakest significance in statistical analysis, it was removed from the regression equation for TSP. Consequently, the regression equation for SO2 is presented as

SO2 = -34 * [wind speed] - 11.8 * [temperature] + [relative humidity] + 0.7 * [previous day's SO2 ]

According to this equation, the concentration of SO2 decreases with increasing temperature and wind speed, and the concentration of SO2 increases with increasing relative humidity and previous day's SO2. The regression equation obtained for the TSP concentration is expressed as

TSP = 51.9 - 15 * [wind speed] - 6.4 * [temperature] + 0.3 * [previous day's TSp]

This equation reveals that the TSP concentration decreases with increasing wind speed and temperature but TSP concentration increases with increasing previous day's TSP concentration.

Considering Eqs. (31.4) and (31.5), the measured SO2 and TSP values were compared with the calculated ones. The data set (all values) of 1995-1997 winter season was used for testing model. As seen in Fig. 31.4a, there is a good agreement between predicted and measured values. The good correlation coefficients reflect the effectiveness of this equation as well. However, the equation did not enhance the predictions for TSP (Fig. 31.4b). The lower enhancement in Eq. (31.5) may be due to the fact that the lifetime of different-sized TSP in atmosphere is more unstable than gaseous compounds because of prevailing meteorological factors.

0 500 1000 1500 2000 2500 SO2 concentration of previous day(ig/m3)

0 500 1000 1500 2000 2500 SO2 concentration of previous day(ig/m3)

"E 800-ro 700— 600° 500-§ 400-§ 300-§ 200-1 Z 100

TSP concentration of previous day (ig/m3)

Temperature (°C)

TSP concentration of previous day (ig/m3)

2500 200015001000500

CO

2500 -|

2000-

c

7a

1500-

c

1000-

a c o O

500-

(D

20 40 60 80 100 Relative humidity (%)

°E 800 & 700-■f 600° 500-I 400-ffi 300-§ 200° 1000-

20 40 60 80 Relative humidity (%)

Fig. 31.3 SO2 concentration versus (a) previous day's SO2 concentration; (b) temperature; (c) wind speed; (d) relative humidity. TSP concentration: (e) previous day's TSP concentration; (f) temperature; (g) wind speed; (h) relative humidity.

Fig. 31.3 SO2 concentration versus (a) previous day's SO2 concentration; (b) temperature; (c) wind speed; (d) relative humidity. TSP concentration: (e) previous day's TSP concentration; (f) temperature; (g) wind speed; (h) relative humidity.

17 25 33 41 49 57 65 73 81 89 97105113121129137145153161169177

Predicted SO2 Observed SO2

7 25 33 41 49 57 65 73 81 89 97105113121129137145153161169177185

Predicted TSP Observed TSP

17 25 33 41 49 57 65 73 81 89 97105113121129137145153161169177

Predicted SO2 Observed SO2

7 25 33 41 49 57 65 73 81 89 97105113121129137145153161169177185

Predicted TSP Observed TSP

Fig. 31.4 Measured and predicted (a) SO2 concentration according to Eq. (31.4) and (b) TSP concentration according to Eq. (31.5).

The SO2 concentration has a good dependence and a 71% coefficient of determination; this means that 71% of SO2 depends on wind speed, temperature, relative humidity, and previous day's SO2 concentration, and 29% is indeterminate. As regards the TSP concentration, there is a moderate dependence on wind speed, temperature, relative humidity, and previous day's TSP concentration: 43% of TSP depends on these factors and 57% is indeterminate. Wind speed and temperature are effective meteorological variables in decreasing SO2 and TSP concentrations. It was found that previous day's pollutants concentrations have slight effect for SO2 and TSP concentrations.

It is seen from the literature that the number of meteorological parameters included in regression equations are variable. Witz and Moore (1981) found the regression coefficients varied 0.68-0.73 between air pollutants (CO, NO, NOx, hydrocarbons) and meteorological parameters (wind direction, wind speed, early morning temperature, and frequency of inversions). Ocak et al. (1997) found the regression coefficients between SO2 and meteorological parameters (rainfall, tem perature, sunshine time, wind speed, relative humidity) as 0.43 and the regression coefficients between PM and meteorological parameters (rainfall, temperature sunshine time, wind speed, relative humidity) as 0.27. In a study performed by Cuhadaroglu and Demirci (1997), the regression coefficient was computed between SO2 and meteorological factors (wind speed and humidity) as 0.53 and the coefficient between TSP and meteorological parameters (temperature and humidity) was estimated as 0.56. Oguz et al. (2003) found the regression coefficients between SO2 and meteorological parameters (temperature, sunshine time, wind speed) as 0.66 and the regression coefficients between PM and meteorological parameters (rainfall, sunshine time, wind speed) as 0.53.

There are limited studies in the literature considering the previous day's pollutant concentration in regression equation. Ocak and Demircioglu (2002) calculated that the regression coefficient between SO2 and meteorological parameters (temperature) was 0.73 and between TSP and meteorological parameters (temperature and wind speed), the coefficient estimated was 0.68. Turalioglu et al. (2005) added previous day's SO2 concentration in regression equation and R2 value of 0.92 was found. They added previous day's TSP concentration in the regression equation and R2 value of 0.89 was found.

The most apparent human impact on climate is the building of cities. The construction of every factory, road, office building, and house destroys existing microclimates and creates new ones of great complexity. Urbanization leads to significant changes in the climate in and around most cities. The cities generally are cloudier, foggier, warmer, and wetter than the countryside. The most known urban climate effect is the urban heat island. There are four major causes of urban heat islands: 1. The rocklike materials from which the city is made have large thermal capacities (ability to store heat). 2. Industry, motor vehicles, and domestic heating release large quantities of heat. 3. Increased atmospheric pollution inhibits the loss of upward-directed radiation from the surface. 4. Tall buildings create a three-dimensional structure that alter the flow of air and create a complex geometry for heat exchange (Lutgens and Tarbuck, 1998; Jonsson et al., 2004). Urban temperature rise is caused by waste heat from heating and air conditioning, power generation, industry, and transportation. Many studies have shown that the magnitude of human-made energy in metropolitan areas can be significant fraction of the energy received from the Sun at surface. The blanket of pollutants over a city, including particulate matter, water vapor, and carbon dioxide, contributes to the heat island by absorbing a portion of the upward-directed long wave radiation emitted at the surface and reemitting some of it back toward the surface. It has been considered that the last freeze of winter season can occur several weeks earlier in the center of Erzurum city than in outlying areas due to the heat-island effect.

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